Variational Auto-Regularized Alignment for Sim-to-Real Control
Martin Hwasser,Danica Kragic,Rika Antonova,Martin Hwasser,Danica Kragic,Rika Antonova
General-purpose simulators can be a valuable data source for flexible learning and control approaches. However, training models or control policies in simulation and then directly applying to hardware can yield brittle control. Instead, we propose a novel way to use simulators as regularizers. Our approach regularizes a decoder of a variational autoencoder to a black-box simulation, with the laten...
